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基于RBF网络灵巧机械手的自适应控制.docx

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基于RBF网络灵巧机械手的自适应控制 Title:AdaptiveControlofaDexterousRoboticManipulatorBasedonRBFNetwork Abstract: Inrecentyears,dexterousroboticmanipulatorshavegainedpopularityduetotheirabilitytoperformcomplextaskswithhighprecision.However,achievingprecisecontrolofsuchmanipulatorsremainsachallengingtask.Inthispaper,weproposeanadaptivecontrolstrategybasedontheRadialBasisFunction(RBF)networkforadexterousroboticmanipulator.TheRBFnetworkisutilizedtoapproximatethedynamicmodelofthemanipulator,enablingadaptivecontroltocompensateforuncertaintiesanddisturbances.Simulationresultsdemonstratetheeffectivenessandrobustnessoftheproposedapproachinachievingprecisecontrolofthedexterousroboticmanipulator. 1.Introduction Dexterousroboticmanipulatorshavebecomeessentialinindustrialandresearchsettingsfortheirversatilityandabilitytoperformintricatetasks.However,attainingprecisecontrolofthesemanipulatorsisacriticalchallengeduetouncertainties,disturbances,andnonlineardynamics.Traditionalcontrolmethods,suchasproportional-integral-derivative(PID)control,oftenstruggletoachievetherequiredaccuracy.Inthispaper,weproposeanadaptivecontroltechniquebasedontheRBFnetworktoaddressthesechallengesandenabletheaccuratecontrolofadexterousroboticmanipulator. 2.DexterousRoboticManipulatorModeling Toestablishafoundationforcontroldesign,aprecisemathematicalmodelofthedexterousroboticmanipulatorisderived.Thekinematicanddynamicequationsareformulated,accountingforthemechanicalstructureandjointconfigurationsofthemanipulator.Thederivedmodelservesasareferenceforcontroldesignandanalysis. 3.AdaptiveControlAlgorithm TheadaptivecontrolalgorithmisdesignedbasedontheRBFnetwork,whichisawell-establishedmethodforfunctionapproximation.TheRBFnetworkistrainedtoestimatethedynamicsofthemanipulatorusinginput-outputmeasurementdata.Theadaptivecontrolalgorithmconsistsoftwomainstages:offlinetrainingandonlinecontrol.Duringtheofflinetrainingstage,theRBFnetworkparametersareadjustedtominimizetheerrorbetweentheactualdynamicmodelandtheestimatedmodel.Intheonline